Passing emotional chatbot sessions to the best suited agent

ABSTRACT

Embodiments of the present invention disclose a method for an automated chat bot conversation session and an agent transfer system for the conversation session. The computer receives a user input from a user in an automated chat bot conversation session. The computer analyzes the user input for at least one sentiment, wherein an at least one analysis result is a value assigned to the at least one sentiment contained within the user input. The computer compares the at least one analysis result to a threshold value to determine if the user should be transferred from the automated chat bot conversation session to a conversation session with a suitable agent. The computer then transfers the user to the conversation session with the suitable agent.

BACKGROUND

The present invention relates generally to the field of analyzingsentiments of users of automated chat services, and more particularly todetermining a suited agent to take over for the automated chat bot in anautomated conversation session when certain sentiments are detected.

Companies have substituted the high cost for making skilled personnelavailable to chat with users with an automated chat bot that is trainedto answer specific questions about a topic. The automated chat bots thatare utilized by the companies lack the understanding of human emotions.This has led to user dissatisfaction when interacting with theseautomated chat bots.

BRIEF SUMMARY

Additional aspects and/or advantages will be set forth in part in thedescription which follows and, in part, will be apparent from thedescription, or may be learned by practice of the invention.

Embodiments of the present invention disclose a method for an automatedchat bot conversation session and an agent transfer system for theconversation session. The computer receives a user input from a user inan automated chat bot conversation session. The computer analyzes theuser input for at least one sentiment, wherein an at least one analysisresult is a value assigned to the at least one sentiment containedwithin the user input. The computer compares the at least one analysisresult to a threshold value to determine if the user should betransferred from the automated chat bot conversation session to aconversation session with a suitable agent. The computer then transfersthe user to the conversation session with the suitable agent.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainexemplary embodiments of the present invention will be more apparentfrom the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a functional block diagram illustrating a system for anautomated chat bot session and an agent transfer system for theconversation session, in accordance with an embodiment of the presentinvention.

FIGS. 2A and 2B are flowcharts depicting operational steps to determinea transfer of a chat bot session to an agent and to select the suitedagent based on user sentiments in an automated conversation sessionwithin the environment of FIG. 1, in accordance with an embodiment ofthe present invention.

FIGS. 3A and 3B are flowcharts depicting operation steps for determiningwhether a user should be transferred to an agent within the environmentof FIG. 1, in accordance with an embodiment of the present invention.

FIGS. 4A and 4B are flowcharts depicting operational steps for anotherway to determine whether a user should be transferred to an agent withinthe environment of FIG. 1, in accordance with an embodiment of thepresent invention.

FIG. 5 is a flowchart depicting operational steps for establishing abaseline to determine whether the user should be transferred to an agentwithin the environment of FIG. 1, in accordance with an embodiment ofthe present invention.

FIGS. 6A and 6B are flowcharts depicting operational steps for theexecution of the way to determine whether a user should be transferredto an agent prepared in FIG. 5 within the environment of FIG. 1, inaccordance with an embodiment of the present invention.

FIG. 7 is a flowchart depicting operational steps to select the suitedagent for the user to be transferred to within the environment of FIG.1, in accordance with an embodiment of the present invention.

FIG. 8 illustrates an example of sentiment vector space used todetermine if the sentiment analyzer crosses the transfer threshold,where the present invention can be implemented.

FIG. 9 is a block diagram of components of a mobile device of the systemfor the transfer of a user from an automated chat to an agent within theenvironment of FIG. 1, in accordance with embodiments of the presentinvention.

FIG. 10 is a block diagram of components of a computing device of thesystem for the transfer of a user from an automated chat to an agentwithin the environment of FIG. 1, in accordance with embodiments of thepresent invention.

FIG. 11 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of exemplaryembodiments of the invention as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the embodiments described hereincan be made without departing from the scope and spirit of theinvention. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used to enablea clear and consistent understanding of the invention. Accordingly, itshould be apparent to those skilled in the art that the followingdescription of exemplary embodiments of the present invention isprovided for illustration purpose only and not for the purpose oflimiting the invention as defined by the appended claims and theirequivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces unless the context clearly dictatesotherwise.

Reference will now be made in detail to the embodiments of the presentinvention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout.

Embodiments of the invention are generally directed to a system for anautomated chat bot session and an agent transfer system for theconversation session. The automated chat bot receives a user input fromthe user computing device. The sentiment analyzer analyzes the userinput to determine whether the user needs to be transferred to an agent.The sentiment analyzer calculates at least one value from the userinputs. When the sentiment analyzer determines that the one or morevalues collected over a period of time indicate a user sentiment thatwarrants transferring the conversation session to an agent, the chattopic and sentiment results are transmitted to the agent selector. Thetransfer to an agent can be triggered by one emotional input and/or byadding up the sentiments in multiple recent user inputs. The agentselector receives past conversation session results, which include userfeedback, agent feedback, chat topics, and past results from thesentiment analyzer, in order to determine a suited agent to take overthe conversation session. The suited agent for the conversation sessionis skilled in the topic and has a history of ending conversationsessions, that have similar sentiments as the one being transferred,with a positive outcome. The agent selector returns a ranking list ofsuitable agents for the user to choose from. When an agent is notavailable to chat, the user can schedule an appointment to chat with theagent, schedule a phone call, or send the agent an email. After theconversation session is completed, feedback is solicited from the userand the agent. The results from the feedback are stored in a historicchat database for future use in the agent selection process.

FIG. 1 is a functional block diagram illustrating a system for anautomated chat bot session and an agent transfer system for theconversation session 100, in accordance with an embodiment of thepresent invention.

The system for an automated chat bot session and an agent transfersystem for the conversation session 100 includes a user computing device120, an agent computing device 130, and a server 140. The user computingdevice 120, the agent computing device 130, and the server 140 are ableto communicate with each other, via a network 110.

The network 110 can be, for example, a local area network (LAN), a widearea network (WAN) such as the Internet, or a combination of the two,and can include wired, wireless, or fiber optic connections. In general,the network 110 can be any combination of connections and protocols thatwill support communications between the user computing device 120, theagent computing device 130, and the server 140, in accordance with oneor more embodiments of the invention.

The user computing device 120 may be any type of computing device thatis capable of connecting to the network 110, for example, a laptopcomputer, tablet computer, netbook computer, personal computer (PC), adesktop computer, a smart phone, or any programmable electronic devicesupporting the functionality required by one or more embodiments of theinvention. The user computing device 120 may include internal andexternal hardware components, as described in further detail below withrespect to FIG. 9 or FIG. 10. In other embodiments, the user computingdevice 120 may operate in a cloud computing environment, as described infurther detail below with respect to FIG. 11 and FIG. 12.

The user computing device 120 represents a computing device thatincludes a user interface, for example, a graphical user interface 122.The graphical user interface 122 can be any type of application thatcontains the interface to transmit a user input to the chat servicemodule 148 and/or receive a user input from the chat service module 148.The user computing device 120 further includes an acoustic device 124that is able to play an auditory chat message. Furthermore, the acousticdevice 124 is able to receive verbal inputs from the user.

The agent computing device 130 may be any type of computing device thatis capable of connecting to the network 110, for example, a laptopcomputer, tablet computer, netbook computer, personal computer (PC), adesktop computer, a smart phone, or any programmable electronic devicesupporting the functionality required by one or more embodiments of theinvention. The agent computing device 130 may include internal andexternal hardware components, as described in further detail below withrespect to FIG. 9 or FIG. 10. In other embodiments, the agent computingdevice 130 may operate in a cloud computing environment, as described infurther detail below with respect to FIG. 11 and FIG. 12.

The agent computing device 130 represents a computing device thatincludes a user interface, for example, a graphical user interface 132.The graphical user interface 132 can be any type of application thatcontains the interface to transmit a user input to the chat servicemodule 148 and/or receive a user input from the chat service module 148.The agent computing device 130 further includes an acoustic device 134that is able to play an auditory chat message. Furthermore, the acousticdevice 134 is able to receive verbal inputs from the agent.

The server 140 includes a communication module 142, a historic chatdatabase 144, an agent database 146, a chat service module 148, anautomated chat bot 150, a sentiment analyzer module 152, an agentselector module 154, a scheduling module 156, and a feedback module 158.The server 140 is able to communicate with the user computing device 120and the agent computing device 130, via the network 110. The server 140may include internal and external hardware components, as depicted anddescribed in further detail below with reference to FIG. 10. In otherembodiments, the server 140 may include internal and external hardwarecomponents, as depicted and described in further detail below withrespect to FIG. 11, and operate in a cloud computing environment, asdepicted in FIG. 12.

The communication module 142 is capable of receiving inputs from theuser computing device 120 and/or the agent computing device 130 andtransmitting a bot input from the automated chat bot 150 to the usercomputing device 120 and/or agent computing device 130, via the network110.

The historic chat database 144 and the agent database 146 are both datastores that store previously obtained data. The historic chat database144 stores previous chat transcripts from the automated chat botconversation sessions conducted by an agent and/or the chat servicemodule 148, user feedback, agent feedback, and the numeric results ofthe sentiment analyzer for each historic chat. The historic chatdatabase 144 receives agent feedback and user feedback from the feedbackmodule 158. The agent database 146 includes agent skill, agentidentification number, agent photo, agent phone number, agent email, andany other information corresponding to an agent.

The chat service module 148 is the chat service application. The usercomputing device 120 transmits and receives inputs to and from the chatservice module 148 to be displayed on the graphical user interface 122.The agent computing device 130 also transmits and receives inputs to andfrom the chat service module 148 to be displayed on the graphical userinterfaces 122 and 132. The chat service module 148 transmits userinputs to the automated chat bot 150 and then receives the response tothe users input from the automated chat bot 150. The automated chat bot150 is an automatic program that conducts a conversation via auditory ortextual methods. The automated chat bot 150 will be described in furtherdetail below. The chat service module 148 transmits the user input tothe sentiment analyzer module 152 for sentiment analysis and/or thesentiment analyzer module 152 captures the user inputs in to theconversation session. The sentiment analyzer module 152 will bedescribed in further detail below. The chat service module 148 receivesa list of agents from the agent selector module 154 and theiravailability from the scheduling module 156. The chat service module 148determines whether a user input is part of a new conversation session orwhether it is part of an existing conversation session. The chat servicemodule 148 determines if the conversation session is complete based on acompletion signal (chat window closed) from the user computing device120 and/or agent computing device 130.

The automated chat bot 150 is a computer program which conducts aconversation via auditory or textual methods. The automated chat bot 150is designed to simulate how a human would behave as a conversationalpartner. The automated chat bot 150 is used in dialog systems forcustomer service, information acquisition, and other such uses. Theautomated chat bot 150 receives an input from the chat service module148 and scans for keywords within the user input and/or determines thetopic and intent of the input using human language classifiers (forexample, IBM Watson™ Natural Language Classifier). The automated chatbot 150 then pulls a reply with the most matching keywords, and/or basedon the identified topic and user intent, from a database (not shown).The database is stored within the automated chat bot 150. The automatedchat bot 150 then transmits the response to the chat service module 148.The automated chat bot 150 also receives user inputs from the sentimentanalyzer module 152 when the sentiment analyzer module 152 determinesthat the user input does not trigger a transfer to an agent.

The sentiment analyzer module 152 receives user inputs from the chatservice module 148. The sentiment analyzer module 152 analyzes each userinput and gives a value to the user input for certain sentiments, suchas anger, disgust, fear, joy, sadness, or other sentiments, and/or anycombination thereof. The sentiment analyzer module 152 can analyze theuser input in different ways in order to determine whether the usershould be transferred to an agent. One way that the sentiment analyzermodule 152 can analyze a user input is by establishing a predeterminedsentiment threshold value for each sentiment, and counting the number oftimes those sentiment threshold values were exceeded. The sentimentanalyzer module 152 increasing a counter by one for each at least onesentiments that exceeds the threshold value for the at least onesentiment. The sentiment analyzer module 152 compares the number on thecounter to a cumulative threshold value, such that, the conversationsession is transferred to the suitable agent when the number of thecounter is greater than the cumulative threshold value.

Another way the sentiment analyzer module 152 can analyze a user inputin order to determine whether a user should be transferred to an agentis by establishing, during system preparation, a plurality of bandnumbers for each of a plurality of sentiments, wherein each band numberhas a specific sentiment range. Sentiment ranges are non-overlapping andsubdivide the output range of the sentiment analyzer for the chosensentiments. The sentiment analyzer module 152 then establishes aswitching score to each of the plurality of band numbers for each of theplurality of sentiments. The sentiment analyzer module 152 assigns aswitching score to the user input by adding up the switching scores foreach sentiment. When the sum of switching scores accumulated over apredetermined number of user inputs exceeds a threshold, theconversation session is transferred to an agent. Different sentimentshave different band numbers and different switching scores associatedwith the band numbers, respectively.

Another way the sentiment analyzer module 152 can analyze a user inputin order to determine whether a user should be transferred to an agentis by creating sentiment vectors for user inputs. During systempreparation, the sentiment analyzer module 152 creates sentiment vectorsfrom past user inputs in historic chats from the historic chat database144 and establishes a switching score for each vector based on theassociated user input. An administrator who assesses the mood of thosehistoric inputs (“if the user says this, how urgent is it to transferthe session”) and accordingly assigns a switching score via thesentiment analyzer module 152. During execution, the sentiment analyzermodule 152 determines the switching score of a current user input byaveraging the switching scores of the nearest neighbor vectors ofhistoric user inputs in sentiment space. In sentiment space, see, forexample, FIG. 8, the nearest neighbors to the current conversationsession sentiment vector, which represent historic user inputs withsimilar sentiments, are found from the historic chat database 144. Theswitching scores of the nearest neighbor sentiment vectors are averagedto get the switching score of the current user input. The sentimentanalyzer module 152 adds switching scores of a predetermined number ofpast user inputs together when there is more than one user input. Whenthe sum exceeds a predetermined transfer threshold value, the results ofthe sentiment analysis are transferred to the agent selector module 154.When the sum does not exceed the predetermined transfer threshold value,the user input is sent to the automated chat bot 150 and the automatedchat bot session continues. When one user input does not exceed thepredetermined transfer threshold value, the sentiment analyzer module152 is able to analyzes a predetermined number of past user inputs todetermine if the total sum exceeds the predetermined threshold value.

The agent selector module 154 receives the sentiment analysis resultsfrom the sentiment analyzer module 152. The agent selector module 154receives the chat topic from the chat service module 148. The agentselector module 154 retrieves the historical conversation sessions fromthe historic chat database 144 that are closest in topic and sentimentto the current conversation session. The agent selector module 154 alsoreceives agent specific information about the agents of thecorresponding historic chats from the agent database 146. The closesthistoric chats are grouped based on the agents who conducted thosehistorical conversation sessions. The chat outcome rating is determinedby the feedback module 158 and is added up for each group to determinean overall chat outcome rating for each agent. The chat outcome ratingis a score assigned to the historic chats based on feedback gatheredfrom the user and the agent at the conclusion of the conversationsession. The chat outcome rating is discussed in further detail below.The group's overall chat outcome rating is multiplied by a weightingfactor. The weighting factor is calculated by Equation 1:

W=(1f logC)/C   (1)

W is the weighting factor, f is a constant between 0 and 1, whichdetermines the influence of agent experience in their selection, and Cis the number of conversation sessions in the group. When f is 0, theweighting factor is 1/C and the result is the average of the chatoutcome ratings in each group. When f is 1, the average chat outcome ineach group is multiplied by a factor that grows with the logarithm ofthe number of conversation sessions in the group. By adjusting f, theinfluence of agent experience on their selection can be fine-tuned. In asituation where all agents are new, a setting of f=0 may be mostappropriate. In a situation where some agents are very experienced (haveconducted a high number of conversation sessions) a larger value of fcan favor the selection of more experienced agents, even if their chatoutcomes do not exceed those of less experienced agents. The agents thatreceive the highest values when multiplying the overall chat outcomerating by the weighting factor are the agents suited to take over theconversation session. The agent selector module 154 transmits a list ofthe suited agents to the scheduling module 156 to check foravailability. When there are no similar historic chats, a random agentis assigned to the user by the agent selector module 154.

The scheduling module 156 receives a list of agents from the agentselector module 154. The scheduling module 156 determines whether theagents are available to chat. When the agents suited to take over theconversation session are unavailable, the scheduling module 156retrieves the agents' schedules and finds their times of availability.The scheduling module 156 transmits whether an agent is available and/ortimes when the agent is available to the chat service module 148. Thescheduling module 156 retrieves the agents' email addresses from theagent database 146 and transmits their email addresses to the chatservice module 148 as well. The chat service module 148 then offersalternative communication channels to the user, for example, an emailaddress to contact the agent in writing and/or the availability of thesuited agents for scheduling a conversation session or call at a futurepoint in time.

The feedback module 158 transmits a feedback request to the usercomputing device 120 and to the agent computing device 130 once thatconversation session has been completed. The feedback request includesquestions regarding the outcome of the conversation session, whether theissue was resolved, and other such questions. Based on the responses tothe feedback questions, first chat outcome rating from the user iscalculated. In addition, the agents rate the conversation session,evaluating criteria such as business impact, revenue opportunity, andother such criteria. The agent's chat rating of the conversation sessionis the second chat outcome rating. The chat outcome rating assigned bythe user and the agent are normalized and then averaged together. Aweighted average of the chat outcome rating of the agent and the usercould be taken to show the significance of one over the other when theoutcome of the conversation session is extreme, such as when a user isdissatisfied, satisfied, and/or when the opinion of the chat outcome ofthe agent and the user are different. For example, the feedback requestcould ask the agent and user a series of questions where an answer of 0is highly dissatisfied and an answer of 10 is highly satisfied. Thefeedback module 158 transmits the overall outcome rating to the historicchat database 144 to store with the chat transcript.

FIGS. 2A and 2B represent the sentiment analyzer module 152 analyzing auser input from the user computing device 120 to determine whether theuser should be transferred to an agent and the agent selector module 154determining agents to take over the conversation session.

FIG. 2A illustrates the sentiment analyzer module 152 analyzing a userinput from the user computing device 120 and FIG. 2B illustrates theagent selector module 154 determining agents to take over theconversation session. The chat service module 148 receives an input fromthe user computing device 120, via the graphical user interface 122and/or the acoustic device 124 (S200). The chat service module 148determines if the user input is a new conversation (S202). When the userinput is a new conversation, the chat service module 148 starts a newconversation session (S204) and the chat service module 148 adds theuser input to the conversation session (S206). When the user input isnot a new conversation, the chat service module 148 adds the user inputto the conversation session (S206). The sentiment analyzer module 152analyzes the sentiment of the user input (S208). The sentiment analyzermodule 152 determines whether the user should be transferred to an agentbased on the analysis of the user input (S210). The sentiment analyzermodule 152 determines that the user should not be transferred to anagent, then the sentiment analyzer module 152 transmits the user inputto the automated chat bot 150 (S212). The automated chat bot 150transmits the chat bot response to the conversation session in the chatservice module 148 (S214). The chat service module 148 then receivesanother input from the user (S200).

The sentiment analyzer module 152 determines that the user should betransferred to an agent, then the sentiment analyzer module 152transmits the user input to the agent selector module 154 and the chatservice module 148 transmits the chat topic to the agent selector module154 (S216). The agent selector module 154 finds suitable agents for thecurrent conversation session by finding agents who have successful chatoutcomes for similar conversation sessions (S218).

The scheduling module 156 receives a ranking list of suitable agents(S220). The scheduling module 156 determines if the agents on the listare available to chat (S222). When there are agents on the list that areavailable to chat, the chat service module 148 determines if the userwant to chat with an available agent (S224). When the user does not wantto chat with an available agent, the chat service module 148 transmitsthe user input to the automated chat bot 150 (S212). When the user doeswant to chat with an available agent, the chat service module 148transfers the user to the agent on the agent computing device 130(S226). The chat service module 148 determines if the conversationsession is complete (S228). When the conversation session is notcomplete, the chat service module 148 continuously determines whetherthe conversation session is complete (S228).

When the agents on the list are not available to chat, the chat servicemodule 148 determines if the user would like to schedule a conversationsession, schedule a call, or send an email (S230). When the user doesnot want to schedule a conversation session, schedule a call, or send anemail to an agent, the chat service module 148 transmits the user inputto the automated chat bot 150 (S212). When the user does want toschedule a conversation session, or send an email to an agent, the chatservice module 148 receives a chosen appointment time from the user orthe scheduling module 156 transmits the email address of the agent tothe user to allow the user to schedule a conversation session with theagent or to conduct the conversation through an email correspondence(S232). When the conversation session is complete, the feedback module158 solicits feedback from the user and the agent (S234). The feedbackmodule 158 receives feedback from the agent and/or the user (S236).Agent feedback is only solicited if an agent participated in theconversation session. The feedback module 158 adds the agentidentification number and the received feedback to the historic chatdatabase 144 (S238).

FIGS. 3A and 3B represent the sentiment analyzer module 152 determiningwhether the user should be transferred to an agent.

FIG. 3A illustrates the sentiment analyzer module 152 determiningwhether the user should be transferred to an agent and FIG. 3Billustrates the agent selector module 154 determining agents to takeover the conversation session. During system preparation, the sentimentanalyzer module 152 chooses sentiments that would trigger a transfer toan agent, such as anger, disgust, fear, joy, sadness, or othersentiments, and/or any combination thereof (S300). The sentimentanalyzer module 152 establishes a predetermined sentiment thresholdvalue for each sentiment (S302). The sentiment analyzer module 152establishes a cumulative count of sentiment threshold crossings byincreasing a counter by one for each sentiment that exceeds thethreshold value for the at least one sentiment, to trigger a transfer toan agent based on the number of sentiment values exceeding thecorresponding predetermined sentiment threshold values (S304). Steps300-304 are established during the initial set up of the sentimentanalyzer module 152, and/or steps 300-304 can be established duringactivate conversation sessions to utilize up to date data. The sentimentanalyzer module 152 receives a user input from the chat service module148 (S306). The sentiment analyzer module 152 counts the number ofsentiments that exceed each of the predetermined sentiment thresholdsfrom the input (S308). The sentiment analyzer module 152 determines ifthere is more than one input (S310). When there is more than one input,the sentiment analyzer module 152 counts the number sentiments thatexceed each of the predetermined sentiment thresholds from each of thelast N inputs, where N represents the number of inputs to be used foruser sentiment analysis, or from all user inputs when there are fewerthan N inputs (S312). When there is not more than one input or whenthere are multiple inputs, the sentiment analyzer module 152 adds up thetotal number of sentiments that exceeded each of the predeterminedsentiment thresholds in the last N inputs or in all of the user inputswhen there are fewer than N inputs (S314). The sentiment analyzer module152 determines if the user should be transferred to an agent based onwhether or not the sum of the number of sentiments that have a sentimentvalue that exceeds their predetermined sentiment threshold. When the sumof the number of sentiments exceeds the cumulative threshold value, thenthe sentiment analyzer module 152 determines that the user should betransferred to an agent (S316). The sentiment analyzer module 152determines that the user should not be transferred to an agent, then thesentiment analyzer module 152 transmits the user input to the automatedchat bot 150 so the user can continue the conversation session with theautomated chat bot (S318). The sentiment analyzer module 152 determinesthat the user should be transferred to an agent, then the sentimentanalyzer module 152 transmits the user input to the agent selectormodule 154 and the chat service module 148 transmits the chat topic tothe agent selector module 154 (S320). The agent selector module 154finds suitable agents for the current conversation session by findingagents who have successful chat outcomes for similar conversationsessions (S322).

The scheduling module 156 receives a ranking list of suitable agents(S324). The scheduling module 156 determines if the agents on the listare available to chat (S326). When there are agents on the list that areavailable to chat, the chat service module 148 determines if the userwant to chat with an available agent (S328). When the user does not wantto chat with an available agent, the chat service module 148 transmitsthe user input to the automated chat bot 150 so the user can continue tochat with the automated chat bot (S318). When the user does want to chatwith an available agent, the chat service module 148 transfers the userto the agent on the agent computing device 130 (S330). The chat servicemodule 148 determines if the conversation session is complete (S332).When the conversation session is not complete, the chat service module148 continuously determines whether the conversation session is complete(S332).

When the agents on the list are not available to chat, the chat servicemodule 148 determines if the user would like to schedule a conversationsession, schedule a call, or send an email (S334). When the user doesnot want to schedule a conversation session, schedule a call, or send anemail to an agent, the chat service module 148 transmits the user inputto the automated chat bot 150 so the user can continue to chat with theautomated chat bot (S318). When the user does want to schedule aconversation session, schedule a call, or send an email to an agent, thechat service module 148 receives a chosen appointment time from the useror the scheduling module 156 transmits the email address of the agent tothe user to allow the user to schedule a conversation session or callwith the agent or to conduct the conversation through an emailcorrespondence (S336). When the conversation session is complete, thefeedback module 158 solicits feedback from the user and the agent(S338). Agent feedback is only solicited if an agent participated in theconversation session. The feedback module 158 receives feedback from theagent and/or the user (S340). The feedback module 158 adds the agentidentification number and the received feedback to the historic chatdatabase 144 (S342).

FIGS. 4A and 4B represent the sentiment analyzer module 152 determiningwhether the user should be transferred to an agent.

FIG. 4A illustrates the sentiment analyzer module 152 determiningwhether the user should be transferred to an agent and FIG. 4Billustrates the agent selector module 154 determining agents to takeover the conversation session. The sentiment analyzer module 152 choosessentiments that would trigger a transfer to an agent, such as anger,disgust, fear, joy, sadness, or other sentiments, and/or any combinationthereof (S400). The sentiment analyzer module 152 establishes aplurality of band numbers for each of a plurality of sentiments, whereineach band number has a specific sentiment range (S402). The sentimentranges are non-overlapping and cover the possible range of output valuesof the sentiment analyzer for the chosen sentiments. The sentimentanalyzer module 152 establishes a switching score to each of theplurality of band numbers for each of the plurality of sentiments(S404). The sentiment analyzer module 152 establishes a threshold totrigger a transfer to an agent (S406). Steps 400-406 are establishedduring the initial set up of the sentiment analyzer module 152, and/orsteps 400-406 can be established during activate conversation sessionsto utilize up to date data. The sentiment analyzer module 152 receives auser input from the chat service module 148 (S408). The sentimentanalyzer module 152 adds up the switching scores for each sentiment(S410). The sentiment analyzer module 152 determines if there is morethan one input (S412). When there is more than one input, the sentimentanalyzer module 152 adds up the switching scores for each of the last Ninputs, where N represents the number of inputs to be used for sentimentanalysis, or all user inputs if fewer than N inputs have been received(S414). When there is not more than one input or when multiple inputsare analyzed, the sentiment analyzer module 152 adds up the totalswitching scores for the last N inputs analyzed (S416). The sentimentanalyzer module 152 determines if the user should be transferred to anagent based on whether or not the total switching score exceeded thevalue to trigger a transfer (S418). The sentiment analyzer module 152determines that the user should not be transferred to an agent, then thesentiment analyzer module 152 transmits the user input to the automatedchat bot 150 so the user can continue the conversation session with theautomated chat bot (S420). The sentiment analyzer module 152 determinesthat the user should be transferred to an agent, then the sentimentanalyzer module 152 transmits the user input to the agent selectormodule 154 and the chat service module 148 transmits the chat topic tothe agent selector module 154 (S422). The agent selector module 154finds suitable agents for the current conversation session by findingagents who have successful chat outcomes for similar conversationsessions (S424).

The scheduling module 156 receives a ranking list of suitable agents(S426). The scheduling module 156 determines if the agents on the listare available to chat (S428). When there are agents on the list that areavailable to chat, the chat service module 148 determines if the userwant to chat with an available agent (S430). When the user does not wantto chat with an available agent, the chat service module 148 transmitsthe user input to the automated chat bot 150 so the user can continue tochat with the automated chat bot (S420). When the user does want to chatwith an available agent, the chat service module 148 transfers the userto the agent on the agent computing device 130 (S432). The chat servicemodule 148 determines if the conversation session is complete (S434).When the conversation session is not complete, the chat service module148 continuously determines whether the conversation session is complete(S434).

When the agents on the list are not available to chat, the chat servicemodule 148 determines if the user would like to schedule a conversationsession, schedule a call, or send an email (S436). When the user doesnot want to schedule a conversation session, schedule a call, or send anemail to an agent, the chat service module 148 transmits the user inputto the automated chat bot 150 so the user can continue to chat with theautomated chat bot (S420). When the user does want to schedule aconversation session, schedule a call, or send an email to an agent, thechat service module 148 receives a chosen appointment time from the useror the scheduling module 156 transmits the email address of the agent tothe user to allow the user to schedule a conversation session with theagent or to conduct the conversation through an email correspondence(S438). When the conversation session is complete, the feedback module158 solicits feedback from the user and the agent (S440). Agent feedbackis only solicited if an agent participated in the conversation session.The feedback module 158 receives feedback from the agent and/or the user(S442). The feedback module 158 adds the agent identification number andthe received feedback to the historic chat database 144 (S444).

FIG. 5 represents a baseline being established by the sentiment analyzermodule 152 to determine whether the user should be transferred to anagent.

FIG. 5 illustrates the sentiment analyzer module 152 establishing abaseline to determine whether the user should be transferred to an agentby constructing sentiment vector space. The sentiment analyzer module152 receives chat transcripts similar in topic from the historic chatdatabase 144 (S500). The sentiment analyzer module 152 receives a userinput from the retrieved historic transcripts (S502). The sentimentanalyzer module 152 creates a sentiment vector from the sentimentanalysis of the user input (S504). The sentiment analyzer module 152presents the input to an administrator who assigns a switching scorebased on assessing how many similar user inputs should cause a sessiontransfer to an agent (S506). Only after the space is populated withsentiment vectors that have a switching score assigned can the score ofnew vectors be calculated by averaging that of its nearest neighbors.The sentiment analyzer module 152 at the time of executing aconversation session determines the switching scores of ongoingconversation sessions. The sentiment analyzer module 152 stores theswitching score along with the sentiment vector in the historic chatdatabase 144 (S508). The chat service module 148 determines if there isanother user input in the transcript (S510). When there is another userinput, the sentiment analyzer module 152 receives the next user inputfrom the retrieved historic transcripts (S502). When the historictranscripts do not contain any more user inputs, the sentiment analyzermodule 152 determines if there is more than one similar chat transcriptin the historic chat database 144 (S512). When there is more than onesimilar chat transcript, the sentiment analyzer module 152 receivessimilar chat transcripts from the historic chat database 144 (S500).

FIGS. 6A and 6B represent the execution of the sentiment analyzer module152 determining whether the user should be transferred to an agent,using the baseline established through the method FIG. 5.

FIG. 6A illustrates the sentiment analyzer module 152 executing thedetermination of whether the user should be transferred to an agent byusing sentiment vector space and FIG. 6B illustrates the agent selectormodule 154 determining agents to take over the conversation session. Thesentiment analyzer module 152 receives the current user input from thechat service module 148 (S600). The sentiment analyzer module 152 runsthe user input through the sentiment analyzer (S602). The sentimentanalyzer module 152 creates a sentiment vector for the user input(S604). The sentiment analyzer module 152 finds the nearest neighbors insentiment vector space (S606). The sentiment analyzer module 152averages the switching scores of the nearest neighbor sentiment vectorsto get the score of the current input (S608). The sentiment analyzermodule 152 adds the averaged switching scores from the last N userinputs, where N represents a predetermined number of user inputs thatare analyzed (S610). For example, the last 5 user inputs may beanalyzed, or all available inputs if the user has not provided 5 or moreinputs. The sentiment analyzer module 152 determines if the sum of theswitching scores is greater than the transfer threshold (S612). When thesum of the switching score is not greater than the transfer threshold,the sentiment analyzer module 152 transmits the user input to theautomated chat bot 150 so the user can continue the conversation sessionwith the automated chat bot (S614). When the sum of the switching scoresis greater than the transfer threshold, the sentiment analyzer module152 transmits the user input to the agent selector module 154 and thechat service module 148 transmits the chat topic to the agent selectormodule 154 (S616). The agent selector module 154 finds suitable agentsfor the current conversation session by finding agents who havesuccessful chat outcomes for similar conversation sessions (S618).

The scheduling module 156 receives a ranking list of suitable agents(S620). The scheduling module 156 determines if the agents on the listare available to chat (S622). When there are agents on the list that areavailable to chat, the chat service module 148 determines if the userwant to chat with an available agent (S624). When the user does not wantto chat with an available agent, the chat service module 148 transmitsthe user input to the automated chat bot 150 so the user can continue tochat with the automated chat bot (S614). When the user does want to chatwith an available agent, the chat service module 148 transfers the userto the agent on the agent computing device 130 (S626). The chat servicemodule 148 determines if the conversation session is complete (S628).When the conversation session is not complete, the chat service module148 continuously determines whether the conversation session is complete(S628).

When the agents on the list are not available to chat, the chat servicemodule 148 determines if the user would like to schedule a conversationsession, schedule a call, or send an email (S630). When the user doesnot want to schedule a conversation session, schedule a call, or send anemail to an agent, the chat service module 148 transmits the user inputto the automated chat bot 150 so the user can continue to chat with theautomated chat bot (S614). When the user does want to schedule aconversation session, scheduler a call, or send an email to an agent,the chat service module 148 receives a chosen appointment time from theuser or the scheduling module 156 transmits the email address of theagent to the user to allow the user to schedule a conversation sessionwith the agent or to conduct the conversation through an emailcorrespondence (S632). When the conversation session is complete, thefeedback module 158 solicits feedback from the user and the agent(S634). Agent feedback is only solicited if an agent participated in theconversation session. The feedback module 158 receives feedback from theagent and/or the user (S636). The feedback module 158 adds the agentidentification number and the received feedback to the historic chatdatabase 144 (S638).

FIG. 7 represents the agent selector module 154 determining the suitedagent to take over the conversation session.

FIG. 7 illustrates the agent selector module 154 finding the suitedagent from past chat transcripts in the historic chat database 144. Theagent selector module 154 receives the sentiment analysis results fromthe sentiment analyzer module 152 (S700). The agent selector module 154receives the topic of the conversation session from the chat servicemodule 148 (S702). The agent selector module 154 determines if there areany similar historic chats by searching the historic chat database 144for conversation sessions similar in topic (S704). When there are notany similar historic chats, the agent selector module 154 assigns arandom agent to the user (S706). When there are similar historic chats,the agent selector module 154 receives agent skill data from the agentdatabase 146 (S708). The agent selector module 154 searches the agentdatabase 146 for agents with high success outcomes for similarconversation sessions (S710). The agent selector module 154 findsnearest neighbor historic chats, which are closest to the sentimentanalysis results for the current conversation session (S712). Theconversation sessions are grouped by agents, and the chat outcomeratings are aggregated for each agent, and potentially multiplied by aweighting factor proportional to the log of the number of aggregatedchats, in order to determine the agents' ranking. The agent selectormodule 154 transmits a ranking list of agents to the scheduling module156 (S714). The chat service module 148 allows the user to choose anagent from the list of available agents transmitted by the schedulingmodule 156 (S716).

FIG. 8 represents an example of sentiment vector space in accordancewith the most sophisticated method of determining whether the usershould be transferred to an agent where the present invention is used.

FIG. 8 illustrates three-dimensional space where the axes representsentiments. The sentiments in this example are anger 1002, sadness 1004,and disgust 1006. The stars 1008 represent sentiment vectors for similarhistorical user inputs created by the sentiment analyzer module 152. Thescrolls 1000 indicate the switching score 1010 associated with thesentiment vector (star 1008) next to it. The switching score for aspecific user input sentiment vector is found by averaging the switchingscores of the nearest neighbor historic sentiment vectors. FIG. 5, FIG.6A, and FIG. 6B illustrate the operational steps for preparation andexecution of this method with regard to the sentiment vector spaceillustrated in FIG. 8.

FIG. 9 is a block diagram of components of the user computing device 120and/or the agent computing device 130 for invoking a user environmentbased on a device cover, in accordance with an embodiment of the presentinvention. In an exemplary embodiment, the user computing device 120and/or the agent computing device 130 include one or more processors810, one or more computer-readable RAMs 812, one or morecomputer-readable ROMs 814, and one or more computer-readable tangiblestorage devices 818 on one or more buses 816. One or more operatingsystems 830, one or more apps or programs 832, and one or more userenvironment definitions 834 are stored on the one or morecomputer-readable tangible storage devices 818 for execution by one ormore of the processors 810 via one or more of the RAMs 812 (whichtypically include cache memory). In the illustrated embodiment, each ofthe computer-readable tangible storage devices 818 is a semiconductorstorage device such as ROM 814, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information. Alternatively, each of thecomputer-readable tangible storage devices 818 is a magnetic diskstorage device of an internal hard drive.

The user computing device 120 and/or the agent computing device 130 alsoincludes a read/write (R/W) interface 822, for example, a USB port, toread from and write to external computing devices or one or moreportable computer-readable tangible storage devices such as a CD-ROM,DVD, memory stick, magnetic disk, optical disk or semiconductor storagedevice. The apps and programs 832 and the user environment definitions834 can be stored on the external computing devices or one or more ofthe portable computer-readable tangible storage devices, read via theR/W interface 822 and loaded onto the computer-readable tangible storagedevice 818.

The user computing device 120 and/or the agent computing device 130 alsoincludes a network adapter or interface 820, such as a TCP/IP adaptercard or wireless communication adapter (such as a 4G wirelesscommunication adapter using OFDMA technology). The apps and programs 832and the user environment definitions 834 can be downloaded to the usercomputing device 120 and/or the agent computing device 130 from anexternal computer or external storage device via a network (for example,the Internet, a local area network, a wide area network, or a wirelessnetwork) and network adapter or interface 820. From the network adapteror interface 820, the apps and programs 832 and the user environmentdefinitions 834 are loaded into computer-readable tangible storagedevice 818. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

The user computing device 120 and/or the agent computing device 130 alsoincludes a touch screen 826, a camera 836, sensors 828, for example,touch screen sensors and magnetically sensitive circuits, and devicedrivers 824 to interface to touch screen 826 for imaging, to sensors 828for pressure sensing of alphanumeric character entry and user selectionsand for detecting magnetic flux and polarity. The device drivers 824,R/W interface 822 and network adapter or interface 820 comprise hardwareand software (stored in computer-readable tangible storage device 818and/or ROM 814).

It should be appreciated that FIG. 9 provides only an illustration ofone implementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environment may be made.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method and program producthave been disclosed for selecting a user environment based on a devicecover. However, numerous modifications and substitutions can be madewithout deviating from the scope of the present invention. Therefore,the present invention has been disclosed by way of example and notlimitation.

FIG. 10 depicts a block diagram of components of the user computingdevice 120 and/or the agent computing device 130 of the system fordetermining a transfer to an agent and selecting the suited agent basedon sentiments in an conversation session 100 of FIG. 1, in accordancewith an embodiment of the present invention. It should be appreciatedthat FIG. 10 provides only an illustration of one implementation anddoes not imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environment may be made.

The user computing device 120 and/or the agent computing device 130and/or the server 140 may include one or more processors 902, one ormore computer-readable RAMs 904, one or more computer-readable ROMs 906,one or more computer readable storage media 908, device drivers 912,read/write drive or interface 914, network adapter or interface 916, allinterconnected over a communications fabric 918. The network adapter 916communicates with a network 930. Communications fabric 918 may beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 910, and one or more application programs911, for example, the chat service module 148 (FIG. 1), are stored onone or more of the computer readable storage media 908 for execution byone or more of the processors 902 via one or more of the respective RAMs904 (which typically include cache memory). In the illustratedembodiment, each of the computer readable storage media 908 may be amagnetic disk storage device of an internal hard drive, CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk, asemiconductor storage device such as RAM, ROM, EPROM, flash memory orany other computer-readable tangible storage device that can store acomputer program and digital information.

The user computing device 120 and/or the agent computing device 130and/or the server 140 may also include a R/W drive or interface 914 toread from and write to one or more portable computer readable storagemedia 926. Application programs 911 on the user computing device 120and/or the agent computing device 130 and/or the server 140 may bestored on one or more of the portable computer readable storage media926, read via the respective R/W drive or interface 914 and loaded intothe respective computer readable storage media 908.

The user computing device 120 and/or the agent computing device 130and/or the server 140 may also include a network adapter or interface916, such as a Transmission Control Protocol (TCP)/Internet Protocol(IP) adapter card or wireless communication adapter (such as a 4Gwireless communication adapter using Orthogonal Frequency DivisionMultiple Access (OFDMA) technology). Application programs 911 on theuser computing device 120 and/or the agent computing device 130 and/orthe server 140 may be downloaded to the computing device from anexternal computer or external storage device via a network (for example,the Internet, a local area network or other wide area network orwireless network) and network adapter or interface 916. From the networkadapter or interface 916, the programs may be loaded onto computerreadable storage media 908. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

The user computing device 120 and/or the agent computing device 130and/or the server 140 may also include a display screen 920, a keyboardor keypad 922, and a computer mouse or touchpad 924. Device drivers 912interface to display screen 920 for imaging, to keyboard or keypad 922,to computer mouse or touchpad 924, and/or to display screen 920 forpressure sensing of alphanumeric character entry and user selections.The device drivers 912, R/W drive or interface 914 and network adapteror interface 916 may comprise hardware and software (stored on computerreadable storage media 908 and/or ROM 906).

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 11, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 9 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 12, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 9) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and chat service module 96.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of thepresent invention. Therefore, the present invention has been disclosedby way of example and not limitation.

While the invention has been shown and described with reference tocertain exemplary embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the presentinvention as defined by the appended claims and their equivalents.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the one or more embodiment, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for an automated chat bot conversationsession and an agent transfer system for the conversation session, themethod comprises: receiving, by a computer, a user input from a user inan automated chat bot conversation session; analyzing, by the computer,the user input for at least one sentiment contained within the userinput, wherein an at least one analysis result is a value assigned tothe at least one sentiment; comparing, by the computer, the at least oneanalysis result to a threshold value to determine when to transfer theuser from the automated chat bot conversation session to a conversationsession with a suitable agent; and transferring, by the computer, theuser to the conversation session with the suitable agent, when the valueof the at least one analysis result exceeds the threshold value.
 2. Themethod of claim 1, wherein the comparing, by the computer, the at leastone analysis result to the threshold value, further comprises:increasing, by the computer, a counter by one for each of a plurality ofsentiments that exceed the threshold value for the at least onesentiment, respectively; comparing, by the computer, the number on thecounter to a cumulative threshold value; and in response to the numberof the counter exceeding the cumulative threshold value, transferring,by the computer, the user to the conversation session with the suitableagent.
 3. The method of claim 1, wherein the at least one analysisresult further comprises: assigning, by the computer, a band number forthe at least one sentiment, wherein the band number comprises a range ofvalues assigned to the at least one sentiment; and assigning, by thecomputer, a switching score to the band number for the at least onesentiment.
 4. The method of claim 3, further comprises: determining, bythe computer, a sum of the switching scores by adding the switchingscore for each of the at least one sentiment in the user input; andtransferring, by the computer, the user to the conversation session withthe suitable agent when the sum of the switching score exceeds aswitching score threshold that triggers the transfer to the conversationsession with the suitable agent, wherein the threshold value is theswitching score threshold.
 5. The method of claim 1, further comprises:determining, by the computer, a chat topic for the conversation session;and retrieving, by the computer, at least one similar historic chattranscripts from a database, wherein the similar historic transcripts isbased on the at least one analysis result and the determined chat topic.6. The method of claim 5, wherein the analyzing, by the computer, theuser input, further comprises: creating, by the computer, a sentimentvector in sentiment vector space for the at least one sentimentcontained within the user input; assigning, by the computer, a switchingscore to the sentiment vector for the at least one sentiment containedwithin the user input by averaging a switching score for each of aplurality of the nearest neighbor historic chat sentiment vectors insentiment vector space; and determining, by the computer, whether theaveraged switching score exceeds the threshold value that triggers thetransfer to the conversation session with the suitable agent.
 7. Themethod of claim 5, further comprises: determining, by the computer, aplurality of suitable agents to respond to the user input, wherein eachof the plurality of the suitable agents is skilled in the determinedchat topic and has provided a positive outcome in a plurality of similarconversation sessions based on the retrieved historic chats; andranking, by the computer, the plurality of suitable agents based onprovided a positive outcome in a plurality of similar conversationsbased on the retrieved historic chats.
 8. The method of claim 7, furthercomprises: determining, by the computer, availability of the pluralityof suitable agents; transmitting, by the computer, to the user theavailability of the plurality of suitable agents and their ranking; andreceiving, by the computer, a user selection of the suitable agent fromthe transmitted availability of the plurality of suitable agents andtheir ranking; wherein the user is transferred to the selected suitableagent.
 9. The method of claim 1, further comprises: determining, by thecomputer, whether the conversation session with the suitable agent iscomplete; in response to determining that the conversation session iscomplete, soliciting, by the computer, a feedback from the user and thesuitable agent that the user conducted the conversation session with;receiving, by the computer, the feedback from the user and the suitableagent that the user conducted the conversation session with, wherein,the feedback reflects a plurality of opinions of the user and thesuitable agent with regard to their satisfaction with the outcome of theconversation session; and storing, by the computer, an identificationnumber of the suitable agent, a conversation transcript based on theconversation between the user and the suitable agent, the user feedback,and the suitable agent feedback in the chat history database.
 10. Themethod of claim 1, wherein the receiving user input further comprisesreceiving a plurality of user inputs, and wherein analyzing, by thecomputer, the user input for at least one sentiment, further compriseanalyzing the each of the plurality of user inputs for at least onesentiment, wherein an at least one analysis result is a value assignedto the at least one sentiment contained within each of the plurality ofuser inputs.
 11. The method of claim 10, wherein the comparing, by thecomputer, the at least one analysis result to the threshold value,further comprises: increasing, by the computer, a counter by one for aplurality of sentiments that exceed the threshold value for the at leastone sentiment; comparing, by the computer, the number on the counter toa cumulative threshold value; and in response to the number of thecounter exceeding the cumulative threshold value, transferring, by thecomputer, the user to the conversation session with the suitable agent.12. The method of claim 10, wherein analyzing the plurality of userinputs further comprises: assigning, by the computer, a band number foreach of the at least one sentiment in each of the plurality of userinputs, wherein the band number comprises a range of values assigned tothe at least one sentiment; assigning, by the computer, a switchingscore to the band number for each of the at least one sentiment in eachof the plurality of user inputs; determining, by the computer, a sum ofthe switching score by adding the switching score for each of the atleast one sentiment in each of the plurality of user inputs; andtransferring, by the computer, the user to the conversation session withthe suitable agent when the sum of the switching score from theplurality of user inputs exceeds the threshold value that triggers thetransfer to the conversation session with the suitable agent.
 13. Themethod of claim 10, further comprises: determining, by the computer, achat topic for the conversation session; retrieving, by the computer, atleast one similar historic chat transcripts from a database, wherein thesimilar historic transcripts is based on the at least one analysisresult and the determined chat topic; wherein analyzing the plurality ofuser inputs further comprises; creating, by the computer, a sentimentvector in sentiment vector space corresponding to each of the pluralityof user inputs; assigning, by the computer, a switching score to thesentiment vector of each of the plurality of user inputs for the atleast one sentiment contained within the user input by averaging aswitching score for a plurality of the nearest neighbor historic chatsentiment vectors in sentiment vector space; determining, by thecomputer, a sum of the switching scores by adding the switching scorefor each of the sentiment vectors corresponding to the plurality of userinputs; and determining, by the computer, whether the sum of theaveraged switching scores exceeds the threshold value that triggers thetransfer to the conversation session with the suitable agent.